1. Field of the Invention
The present invention relates to a method and an apparatus for controlling a process, and, in particular, to such a method and an apparatus that makes use of the information stored in a parallel distributed processing network that is previously trained to simulate the process.
2. Description of the Prior Art
Control of a manufacturing process may be broadly divided into two classes of activity: (1) determining the initial parameter settings or initial constituent concentrations for a process ("batch initialization"); and (2) ongoing adjustment of parameter settings ("on-going process control"). The latter class of activity may itself be subdivided into those adjustments that are able to be made without reference to the history of the process adjustments (termed "static" adjustments) and those adjustments which are dependent upon the preceding process adjustments (termed "dynamical" adjustments). These activities are interrelated in the sense that an initial setting of a parameter or initial concentration of a constituent may be viewed as an adjustment of the variable from its random position or from an undetermined concentration whereas an ongoing adjustment may be viewed as a timed sequence of initial parameter settings.
There is a prevalent attitude that a so-called parallel distributed processing network ("PDPN") may have utility in supervisory control of physical processes. For example B. Widrow discussed the control of two physical processes with feed-forward neural networks at the December 1988 meeting "Neural Information Processing Systems", Denver, Colo.
In such a role the parallel distributed processing network is used as a conceptual model that relates the process input parameters to the output characteristics of the physical process in a feed forward manner. This arrangement is satisfactory if it is desired merely to know how certain predetermined alterations of the inputs would affect the characteristics of the output product. In process control situations, however, what is desired is to know a priori is exactly what the input parameters should be in order to produce an output product having desired characteristics.
Accordingly, in view of the foregoing it is believed desirable to provide a control arrangement using a parallel distributed processing network that would enable an operator to determine what input parameters of a physical process are needed in order to obtain predetermined characteristics in the product of the process, whether used in a batch initialization situation or on-going process control.